1. Field of the Invention
This invention relates to methods and systems for detecting failures or performance degradation in dynamic systems such as flight vehicles.
2. Background Art
FIG. 1 is a representation of the prior art, which depicts failure detection and isolation (i.e., FDI) of flight vehicle icing. Generally, the method of this prior art teaches detection and isolation of failures in flight vehicles that result in a loss of control effectiveness. Detection and isolation of failures is accomplished via a linear state estimator or observer that continuously calculates an estimate {circumflex over (x)} of the state vector x of the flight vehicle dynamic system in question. The flight vehicle dynamic system is assumed to have sensors available for measuring some or all of the state variables. The measured values y are normally present as part of the dynamic system.
The state estimator calculates estimated values ŷ of the sensor outputs and is designed such that for no system failures the estimated values ŷ agree with measured sensor values y. Whenever system failures (as described above) occur, there is a non-zero error e difference between y and ŷ:
e=yxe2x88x92ŷ.
Each state estimator is designed to detect and isolate a particular hypothesized failure mode ƒ.
The feedback gains for the state estimator are chosen such that the error residuals for a given hypothesized failure are in a unique direction in output space. Isolation of the failure from other possible failures is done via the directionality of the error residuals.
The state estimator is a linear Luenberger-type observer which represents a dynamic system that has dynamics typically given by nonlinear mathematical models. The dynamics are typically a linearized model of a nonlinear system. In this case, the models can be obtained from empirical measurement of dynamic system performance through instrumented flight test or through mathematical modeling of the system. The nonlinear models may be obtained from the above data by any of a number of standard regression techniques as is known in the art.
The state estimator is an observer of the form:
{circumflex over ({dot over (x)})}=A{circumflex over (x)}+Bu+L(yxe2x88x92ŷ)
ŷ=C{circumflex over (x)}+Du,
where A and B are the state transition and input matrices, respectively, of the nominal reference model for the system dynamics, and D is the estimator gain matrix that is chosen such that the output error residual e=yxe2x88x92ŷ is one dimensional. The design of the estimator gain is explained in section 2.1 of Appendix A hereto. The nominal matrices A and B are obtained by linearizing the system nonlinear models at an operating point and are approximately valid representations of the dynamic system in a neighborhood of that operating point. Other sets of matrices are required to characterize flight vehicle dynamics over the entire flight envelope.
It was previously recognized by the inventors that some procedure could be found for selecting operating points and associated neighborhoods, and representing the dynamic system within that neighborhood based on certain operating parameters for the dynamic system (e.g., flap setting angles for flight vehicles). This procedure is called xe2x80x9cgain schedulingxe2x80x9d in the prior art papers authored by the inventors.
However, it was not known or shown in the prior art how to select operating points or how large the neighborhoods could be to achieve acceptable error levels for the FDI.
It was also recognized in the prior art papers authored by the inventors and noted herein below that detection and isolation of hypothesized failures could be accomplished by examining the magnitude of error residuals along the direction of the output of the FDI for the hypothesized failure.
U.S. Pat. Nos. 5,615,119; 5,819,188; and 6,085,127 to Vos disclose fault tolerant automatic control systems utilizing analytic redundancy. The systems are used for controlling a dynamic device, preferably a flight vehicle. The systems include a processing structure which controls the operation of the systems. In operation, the processing structure transforms sensed dynamic device control criteria into a linear time invariant coordinate system, determines an expected response for the device according to the transformed control criteria, compares the expected response with a measured response of the device and reconfigures the control means based on the comparison.
U.S. Pat. No. 4,355,358 to Clelford et al. discloses an adaptive flight vehicle actuator fault detection system. The system, utilizing sensors to determine the position of various operating devices within a flight vehicle, compares the positions with expected positions provided by an operating model of the flight vehicle. Thereafter, the system provides fault warnings, based upon the actual device operating conditions and the expected operating conditions obtained from the model.
U.S. Pat. No. 5,919,267 to Urnes et al. discloses a neural network fault diagnostics system and method for monitoring the condition of a host system, preferably a flight vehicle including a plurality of subsystems. The system includes a neural network means for modeling the performance of each subsystem in a normal operating mode and a plurality of different failure modes. The system also includes a comparator means for comparing the actual performance of each subsystem with the modeled performance in each of the normal and possible failure modes. Finally, the system includes a processor for determining, based on the comparisons of the comparator, the operating condition of the host system.
U.S. Pat. No. 5,070,458 to Gilmore et al. discloses a method of analyzing and predicting both airplane and engine performance characteristics. In operation, the system monitors the operation of a flight vehicle during flight and stores the monitored parameters and flight circumstances in a memory. Thereafter, during subsequent flights, the system determines and/or predicts how the flight vehicle should be operating.
U.S. Pat. No. 4,312,041 to DeJonge discloses a flight performance data computer system. In operation, the system monitors the operation of various operating characteristics of a flight vehicle during flight and provides an indication of the characteristics to the flight vehicle operator. The information provided by the system assists the operator during the flight.
U.S. Pat. Nos. 5,195,046; 5,838,261; and 6,052,056 disclose various systems for monitoring the performance of dynamic flight vehicle subsystems and providing an indication of the performance to the flight vehicle operator.
U.S. Pat. No. 3,603,948 discloses fault identification, isolation, and display device for testing a flight vehicle control system. The device senses malfunctions in selected portions of the system and provides a visual display which instantaneously identifies and isolates the malfunctioning section and memorizes the fault status of the section until the device is manually or automatically reset.
U.S. Pat. No. 3,678,256 discloses a performance and failure assessment monitor which assesses overall performance of the operation of the automatic landing mode of a flight control system for a flight vehicle. The monitor is connected to various sensors throughout the flight vehicle so that it can compare what the flight control system of the flight vehicle is accomplishing during a landing maneuver against an independent model generated within the monitor of what the flight control system should be accomplishing. The resultant comparison is displayed to the pilot as a measure of relative confidence that the landing will be accomplished properly. The monitor also includes failure verification and failure reversion control for making immediate and accurate assessments of the consequence of a failure of any component in the flight vehicle which in any way affects the ability of the flight control and flight guidance instrument systems to operate properly, for correcting the failure when possible and for displaying only the critical failure information to the pilot of the flight vehicle.
U.S. Pat. No. 5,760,711 discloses a modulated light source (31) which transmits light pulses via a prism (27) through a monofilament optical fiber light channel (20) to an optical sensor (10) remotely located and flush mounted to an aerodynamic surface (14) of the flight vehicle most likely to accrete ice. In the absence of ice, little to no light is reflected inward via the light channel. When water, ice, or de-icing fluid covers the light sensor, an increased amount of light pulses are reflected inward through the single fiber optic light channel and prism assembly where they are detected by a light detector (34) which generates an electrical output signal indicative of the type, amount, and rate of ice accretion. The output signal is visually displayed (37) and the pilot may be audibly warned. In an alternate embodiment, the remote light sensor (70) is fuselage mounted (72) with an airfoil shaped probe (71) having a clear lucite leading edge (73) to which is secured a pair of fiber optic light pipes (75,76), one for outbound (76) and the other for inbound (75) light pulses. This mode requires no prism assembly. All electronics are housed in the computerized control/display unit (30) other than the fiber optic cable and remote mounted light sensor.
U.S. Pat. No. 5,301,905 discloses a flight vehicle icing detection system which detects accumulation of ice on an upper surface (12) of a wing (10) of a flight vehicle. The system includes an air pump (18) that delivers air through first and second conduits (24,26). The first conduit delivers air through a first air knife (32) to openings (34) in the upper surface of the wing. The second conduit delivers air through a lower wing surface (14) through openings in a second air knife (42). When ice accumulates on the upper surface, flow from the first air knife is restricted. A differential pressure sensor (46) senses a pressure difference between the conduits and warns the pilot of possible ice accumulation by illuminating a warning light (50).
The reference IFAC World Congress, xe2x80x9cParameter Identification for Inflight Detection of Aircraft Icingxe2x80x9d, July 1999, discloses the use of signal processing to detect icing using online parameter estimation. The reference identifies a new model and compares it to a baseline model, rather than looking only for a change in the baseline model.
The following papers authored by the inventors of this application are relevant and are hereby incorporated in their entirety herein:
William Ribbens and Robert H. Miller, xe2x80x9cDetection of Icing and Related Loss of Control Effectiveness in Regional and Corporate Aircraftxe2x80x9d, AVIATION CONFERENCE, SAE, 1999;
Robert H. Miller and William B. Ribbens, xe2x80x9cDetection of the Loss of Elevator Effectiveness Due to Icingxe2x80x9d, Number 99-0637, 37TH AEROSPACE SCIENCES, AIAA, January 1999; and
Robert H. Miller and William B. Ribbens, xe2x80x9cThe Effects of Icing on the Longitudinal Dynamics of an Icing Research Aircraftxe2x80x9d, Number 99-0636, 37TH AEROSPACE SCIENCES, AIAA, January 1999.
Fault detection theory and other background material can be found in Appendix A hereto.
An object of the present invention is to provide an improved method and system for detecting a failure or performance degradation in a dynamic system such as a flight vehicle.
In general, performance degradation means a very small but statistically significant change in a parameter or group of parameters in a mathematical model of the system.
In carrying out the above object and other objects of the present invention, a method for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables of the system and providing corresponding output signals in response to at least one system input signal is provided. The method includes calculating estimated gains of a filter and selecting an appropriate linear model for processing the output signals based on at least one system input signal. The step of calculating utilizes at least one model of the dynamic system to obtain estimated signals. The method also includes calculating output error residuals based on the output signals and the estimated signals. The method further includes detecting at least one hypothesized failure or performance degradation of a component or subsystem of the dynamic system based on the error residuals. The step of calculating the estimated gains is performed optimally with respect to one or more of: noise, uncertainty of parameters of the at least one model and un-modeled dynamics of the dynamic system.
The step of calculating estimated gains may be performed continually.
The dynamic system may be a closed-loop dynamic system.
The method may further include generating a signal for each hypothesized failure or performance degradation and storing each signal in a database for subsequent retrieval.
The method may further include generating a signal for each hypothesized failure or performance degradation and processing each signal to diagnose the at least one hypothesized failure or performance degradation.
The method may further include generating a signal for each hypothesized failure or performance degradation and processing each signal to obtain a reconfiguration signal.
The dynamic system may have a controller wherein the method may further include reconfiguring the controller based on the reconfiguration signal to compensate for the at least one hypothesized failure or performance degradation.
At least one hypothesized failure or performance degradation may be a failure or degradation of one of the sensors.
The reconfiguration signal may insert an estimated or compensated value of the output signal of the failed or degraded sensor into the controller.
The dynamic system may have a controller and at least one actuator wherein the method may further include reconfiguring the controller based on the reconfiguration signal to compensate for a change of the at least one actuator.
The dynamic system may be a flight vehicle and the sensors may include flight control sensors. The dynamic system may also be a financial market or a modeled financial system.
The dynamic system may be a physical system characterized by a nonlinear dynamic model having parameters. The changes in the dynamic system may be manifest by parameter changes in the nonlinear dynamic model.
The error residuals may be propagated in a unique direction in output detection space for a given hypothesized failure or performance degradation.
The step of calculating estimated gains may include the step of controllably selecting parameters of the at least one model.
The step of detecting may detect intermittent faults and may be based on magnitude and direction of the error residuals in the detection space.
At least one model may include non-dimensional variables wherein the step of detecting may include the step of converting from the non-dimensional variables to dimensional variables to obtain re-scaled error residuals and wherein the step of detecting is also based on the re-scaled error residuals.
A plurality of mathematical models may be utilized to model the dynamic system wherein the step of calculating estimated gains may include the step of selecting one of the plurality of mathematical models.
Further in carrying out the above object and other objects of the present invention, a detection system for detecting a failure or performance degradation in a dynamic system having sensors for measuring state variables of the dynamic system and providing corresponding output signals in response to at least one system input signal is provided. The detection system includes means for calculating estimated gains of a filter and choosing an appropriate linear model for processing the output signals based on the at least one input signal. The means for calculating utilizes at least one model of the dynamic system to obtain estimated signals. The system further includes means for calculating output error residuals based on the output signals and the estimated signals. The system also includes means for detecting at least one hypothesized failure or performance degradation of a component or subsystem of the dynamic system based on the error residuals. The means for calculating the estimated gains calculates optimally with respect to one or more of: noise, uncertainty of parameters of the at least one model and un-modeled dynamics of the dynamic system.
The estimated gains may be calculated continually.
The dynamic system may be a closed-loop dynamic system.
The detection system may further include means for generating a signal for each hypothesized failure or performance degradation and a database for storing each signal for subsequent retrieval.
The detection system may further include means for generating a signal for each hypothesized failure or performance degradation and means for processing each signal to diagnose the at least one hypothesized failure or performance degradation.
The detection system may further include means for generating a signal for each hypothesized failure or performance degradation and means for processing each signal to obtain a reconfiguration signal.
The dynamic system may have a controller and the detection system may further include means for reconfiguring the controller based on the reconfiguration signal to compensate for the at least one hypothesized failure or performance degradation.
At least one hypothesized failure or performance degradation may be a failure or degradation of one of the sensors.
The reconfiguration signal may insert an estimated or compensated value of the output signal of the failed or degraded sensor into the controller.
The dynamic system may have a controller and at least one actuator and the detection system may further include means for reconfiguring the controller based on the reconfiguration signal to compensate for a change of the at least one actuator.
The dynamic system may be a flight vehicle and the sensors may include flight control sensors. The dynamic system may also be a financial market or modeled financial system.
The dynamic system may be a physical system characterized by a nonlinear dynamic model having parameters and the changes in the dynamic system may be manifest by parameter changes in the nonlinear dynamic model.
The error residuals may be propagated in a unique direction in output detection space for a given hypothesized failure or performance degradation.
The means for calculating estimated gains may include means for controllably selecting parameters of the at least one model.
The means for detecting may detect intermittent faults.
The means for detecting may detect based on magnitude and direction of the error residuals in the detection space.
At least one model may include non-dimensional variables and the means for detecting may include means for converting from the non-dimensional variables to dimensional variables to obtain re-scaled error residuals and the means for detecting may detect based on the re-scaled error residuals.
A plurality of mathematical models may be utilized to model the dynamic system and the means for calculating estimated gains may include means for selecting one of the plurality of mathematical models.
In general, the method of designing or calculating the filter gains is based upon a optimization problem solving linear matrix inequality optimization problem with a subset of the eigenstructure specified. The optimization metric can be based upon minimizing the variance, the maximum deviation, the infinity norm, or any quadratic or linear cost function.
The above object and other objects, features, and advantages of the present invention are readily apparent from the following detailed description of the best mode for carrying out the invention when taken in connection with the accompanying drawings.